Achieving k-Anonymity by clustering in attribute hierarchical structures

Paper

Li, Jiuyong, Wong, Raymond Chi-Wing, Fu, Ada Wai-Chee and Pei, Jian. 2006. "Achieving k-Anonymity by clustering in attribute hierarchical structures." Tjoa, A. Min and Trujillo, Juan (ed.) 8th International Conference on Data Warehousing and Knowledge Discovery. Krakow, Poland 04 - 08 Sep 2006 Germany. Springer. https://doi.org/10.1007/11823728
Paper/Presentation Title

Achieving k-Anonymity by clustering in attribute hierarchical structures

Presentation TypePaper
AuthorsLi, Jiuyong (Author), Wong, Raymond Chi-Wing (Author), Fu, Ada Wai-Chee (Author) and Pei, Jian (Author)
EditorsTjoa, A. Min and Trujillo, Juan
Journal or Proceedings TitleLecture Notes in Computer Science (Book series)
Journal Citation4081, pp. 405-416
Number of Pages12
Year2006
PublisherSpringer
Place of PublicationGermany
ISSN1611-3349
0302-9743
ISBN9783540377368
Digital Object Identifier (DOI)https://doi.org/10.1007/11823728
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/11823728_39
Conference/Event8th International Conference on Data Warehousing and Knowledge Discovery
Event Details
8th International Conference on Data Warehousing and Knowledge Discovery
Event Date
04 to end of 08 Sep 2006
Event Location
Krakow, Poland
Abstract

Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k-anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we study achieving k-anonymity by clustering in attribute hierarchical structures. We define generalisation distances between tuples to characterise distortions by generalisations and discuss the properties of the distances. We conclude that the generalisation distance is a metric distance. We propose an efficient clustering-based algorithm for k-anonymisation. We experimentally show that the proposed method is more scalable and causes significantly less distortions than an optimal global recoding k-anonymity method.

Keywordsdata mining; privacy preserving; k-anonymity
ANZSRC Field of Research 2020461305. Data structures and algorithms
460908. Information systems organisation and management
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author.

Byline AffiliationsDepartment of Mathematics and Computing
Chinese University of Hong Kong, China
Simon Fraser University, Canada
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